Field
The disclosed embodiments relate to distributed machine learning. More specifically, the disclosed embodiments relate to techniques for providing version control in asynchronous distributed machine learning.
Related Art
Analytics may be used to discover trends, patterns, relationships, and/or other attributes related to large sets of complex, interconnected, and/or multidimensional data. In turn, the discovered information may be used to gain insights and/or guide decisions and/or actions related to the data. For example, business analytics may be used to assess past performance, guide business planning, and/or identify actions that may improve future performance.
However, significant increases in the size of data sets have resulted in difficulties associated with collecting, storing, managing, transferring, sharing, analyzing, and/or visualizing the data in a timely manner. For example, conventional software tools and/or storage mechanisms may be unable to handle petabytes or exabytes of loosely structured data that is generated on a daily and/or continuous basis from multiple, heterogeneous sources. Instead, management and processing of “big data” may require massively parallel software running on a large number of physical servers and/or nodes, as well as synchronization among the servers and/or nodes.
Consequently, big data analytics may be facilitated by mechanisms for efficiently and/or effectively collecting, storing, managing, compressing, transferring, sharing, analyzing, and/or visualizing large data sets.
In the figures, like reference numerals refer to the same figure elements.